by Gen-Verse · Agent Tool · ★ 545
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RLAnything (ICML 2026) & AutoTool (ICML 2026), DemyAgent: Open-Source RL for LLMs and Agentic Scenarios RLAnything (click to expand) RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System An overview of our research on RLAnything. In this work, we propose RLAnything, a reinforcement learning framework that dynamically optimizes each component through close
| Stars | 545 |
| Forks | 54 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 43.2/100 |
| Open Issues | 9 |
| Last Updated | 2026-06-12 |
| Created | 2025-10-13 |
| Platforms | python |
| Est. Tokens | ~13k |
These tools work well together with Open-AgentRL for enhanced workflows:
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🌱 A little course on Reinforcement Learning Environments for evaluating and training Language Models
历年ICLR论文和开源项目合集，包含ICLR2021、ICLR2022、ICLR2023、ICLR2024、ICLR2025.
✨✨Latest Advances on Neuro-Symbolic Learning in the era of Large Language Models
[NeurIPS 2024] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
Zero-friction LLM fine-tuning skill for Claude Code, Gemini CLI & any ACP agent. Unsloth on NVIDIA · TRL+MPS/M
Awesome LLM Papers and repos on very comprehensive topics.
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Open-AgentRL is RLAnything (ICML 2026) & AutoTool (ICML 2026), DemyAgent: Open-Source RL for LLMs and Agentic Scenarios. It is categorized as a Agent Tool with 545 GitHub stars.
Open-AgentRL is primarily written in Python. It covers topics such as agent-rl, coding-agent, entropy-method.
You can find installation instructions and usage details in the Open-AgentRL GitHub repository at github.com/Gen-Verse/Open-AgentRL. The project has 545 stars and 54 forks, indicating an active community.
Open-AgentRL is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to Open-AgentRL on Agent Skills Hub include llm-rl-environments-lil-course, ICLR2025-Papers-with-Code, Awesome-LLM-Reasoning-with-NeSy. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.